Groundwater contaminant source identification based on iterative local update ensemble smoother

Identification of the location and intensity of groundwater pollution source contributes to the effect of pollution remediation, and is called groundwater contaminant source identifcation. This is a kind of typical groundwater inverse problem, and the solution is usually ill-posed. Especially considering the spatial variability of hydraulic conductivity field, the identification process is more challenging. In this paper, the solution framework of groundwater contaminant source identification is composed with groundwater pollutant transport model (MT3DMS) and a data assimilation method (Iterative local update ensemble smoother, ILUES). In addition, Karhunen-Loève expansion technique is adopted as a PCA method to realize dimension reduction. In practical problems, the geostatistical method is usually used to characterize the hydraulic conductivity feld, and only the contaminant source information is inversely calculated in the identifcation process. In this study, the identification of contaminant source information under Kriging K-field is compared with simultaneous identification of source information and K-field. The results indicate that it is necessary to carry out simultaneous identification under heterogeneous site, and ILUES has good performance in solving high-dimensional parameter inversion problems.     

Neural network for wave forecasting among multi-stations

Unlike in the open sea, the use of wind information for forecasting waves may encounter more ambiguous uncertainties in the coastal or harbor area due to the influence of complicated geometric configurations. Thus this paper attempts to forecast the waves based on learning the characteristics of observed waves, rather than the use of the wind information. This is reported in this paper by the application of the artificial neural network (ANN), in which the back-propagation algorithm is employed in the learning process for obtaining the desired results. This model evaluated the interconnection weights among multi-stations based on the previous short-term data, from which a time series of waves at a station can be generated for forecasting or data supplement based on using the neighbor stations data. Field data are used for testing the applicability of the ANN model. The results show that the ANN model performs well for both wave forecasting and data supplement when using a short-term observed wave data.     

大数据环境下地理信息产业发展的几点思考

大数据背景下地理信息产业的发展有了新的驱动力,通过思考这种驱动力对产业发展可能产生的影响,分析指出了大数据在地理信息产业中的中心地位和重要价值,综述了大数据给地理信息产业的涵盖范围、关键技术和服务模式所带来的重大变革,探讨了大数据技术对于GIS传统技术优化升级的几个着力点,并剖析了大数据技术在地理信息产业的相关应用中所面临的制约因素。     

基于卫星资料梯度信息的新型变分同化方法对于台风数值模拟的研究

卫星资料凭着卫星遥感的全球性、连续性和高频次观测等优势,成为一种重要的非常规资料源,但卫星观测仍然存在各种各样的观测误差,其中包含由于观测偶然性所造成的统计学上的随机误差及仪器本身和辐射传输模式等造成的系统性偏差,这些误差在很大程度上影响了卫星资料的质量。文中提出了一种能有效订正卫星观测资料系统性偏差的梯度信息同化算法,该方法用一个梯度算子进行模式变量与观测变量的梯度变换,从而达到订正系统性偏差的目的。本文利用WRF（Weather Research Forecast）模式及其同化模式WRFDA（WRF Data Assimilation system）,以及AIRS（Atmospheric Infrared Sounder）资料,对台风“圆规”进行了实际的数值模拟和同化试验,数值结果表明,梯度信息同化方法能明显改善台风路径的模拟,在处理可信度较低的资料时仍然适用。另外,通过同化诊断分析,发现卫星资料的系统性偏差对于台风数值模拟有较大影响,而文中提出的梯度信息同化方法能较好的解决此类问题。     

江门市GIS建设的矢量化数据处理

结合江门市GIS工程建设中采用Epscan2.1进行地形图扫描矢量化的具体实践,对矢量化数据处理的几个突出问题作了阐述,提出了具体的解决思路与实现方法。     

微气象自动观测网

本文介绍利用计算机和通讯技术,建立局地网络的微气象观测系统,实现气象数据的自动采集、无线传输和集中实时处理。整个系统由一个中心站和五个(或更多)观测子站组成,是一个点到点的通讯网络。该系统为研究不同下垫面地气相互作用的观测实验提供了手段和方便。整个系统分两部份介绍,本文作一总体概述,第二部份说明数据采集和无线传输的具体实施。     

珠江三角洲区域一体化研究综合信息系统的设计与应用

在分析地理信息系统技术发展与应用现状的基础上,设计了珠江三角洲区域一体化研究综合信息系统的结构框架。根据珠江三角洲的实际情况,并在经济地理学,城市地理学,区域科学等理论的指导下,开发了针对该区域一体化研究的模型分析系统。基于元数据库的数据集成与模型管理技术,能对不同数据源,不同数据类型与时空特性的多元数据处理与分析。本综合信息系统在区域产业布局,资源窨组合,城市体系空间结构演化等方面得到应用,将为     

基于数据分布域变换与贝叶斯神经网络的渗透率预测及不确定性估计

渗透率是储层评价和油气藏开发的关键参数.传统测井方法与常规机器学习方法估算的渗透率都是固定值.但由于测井数据本身存在噪声, 渗透率的预测结果可能受到噪声的影响出现测量性的随机误差(即任意不确定性); 同时, 当测试数据与训练数据存在差异时, 机器学习模型在预测渗透率时可能出现模型参数的不确定性(即认知不确定性).为实现渗透率的准确预测并量化两种不确定性对结果的影响, 本文提出基于数据分布域变换和贝叶斯神经网络同时实现渗透率预测及其不确定性的估计.提出方法主要包括两个部分: 一部分是不同域数据分布的相互转换, 另一部分是基于贝叶斯理论的神经网络渗透率建模预测和不确定性估计.由于贝叶斯神经网络存在数据分布的假设, 当标签的概率分布与网络的分布保持一致时, 贝叶斯神经网络可以更好的学习到数据之间的关系.因此通过寻找一个函数将一个原始域的渗透率标签转换为目标域的与渗透率有关的变量(我们称为目标域渗透率), 使得该变量符合贝叶斯神经网络的分布假设.我们使用贝叶斯神经网络预测目标域渗透率以及任意不确定性和认知不确定性.随后, 通过分布域的逆变换, 我们将目标域渗透率还原回原始域渗透率.应用本文方法到某油田的18口井的测井数据中, 使用16口井的数据进行训练, 2口井进行测试.测试井的预测渗透率与真实渗透率基本一致.同时, 任意不确定性的预测结果提供了渗透率预测值受到的测井数据噪声影响的位置.认知不确定的预测结果说明数据量少的位置具有更高的认知不确定性.我们提出的这一流程不仅显示了在储层表征方面的巨大潜力, 同时可以降低测井解释时的风险.

     

评价储层含油性的热解参数校正方法及其应用

评价储层含油性时，热解参数Ｓ１＋Ｓ２是一项有效参数。其中Ｓ２相对稳定，Ｓ１则随样品类别或露置时间的不同而发生变化。岩心样品的随钻解分析值比较接近真实，岩屑和井壁取心样品的Ｓ１会明显损失。而目前多数探井心进尺少，大多为岩屑录井。     

The H.E.S.S. central data acquisition system

The High Energy Stereoscopic System (H.E.S.S.) is a system of Imaging Atmospheric Cherenkov Telescopes (IACTs) located in the Khomas Highland in Namibia. It measures cosmic gamma rays of very high energies (VHE; >100 GeV) using the Earth’s atmosphere as a calorimeter. The H.E.S.S. Array entered Phase II in September 2012 with the inauguration of a fifth telescope that is larger and more complex than the other four. This paper will give an overview of the current H.E.S.S. central data acquisition (DAQ) system with particular emphasis on the upgrades made to integrate the fifth telescope into the array. At first, the various requirements for the central DAQ are discussed then the general design principles employed to fulfil these requirements are described. Finally, the performance, stability and reliability of the H.E.S.S. central DAQ are presented. One of the major accomplishments is that less than 0.8% of observation time has been lost due to central DAQ problems since 2009.